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Ilka DiesterView profile

Professor

• Motor Control
• Cognitive Control
• Prefrontal Cortex
• +5 more

Prof. Dr. Ilka Diester is a Full Professor of Optophysiology at the Faculty of Biology, Albert-Ludwigs-University Freiburg, and Spokesperson of the BrainLinks-BrainTools Center. She serves as Managing Director of IMBIT (Intelligent Machine-Brain Interfacing Technology) and Section Spokesperson for Systems Neurobiology on the Board of the German Neuroscience Society (2025-2027). Her educational background includes a Doctorate in Neurobiology from the University of Tübingen (2003-2008) and a Diploma in Biology from Humboldt University of Berlin (1998-2003). Prior positions include Group Leader at the Ernst Strüngmann Institute (2011-2014) and Postdoc at Stanford University (2008-2011). Her research focuses on the neural basis of motor control and cognitive control , investigating interactions between prefrontal and motor cortex using electrophysiological recordings, optogenetic manipulations, and behavioral analysis. Current projects include developing optoelectronic probes, studying prefrontal flexibility in strategy choice, and examining movement decoding in rodent models. Recent publications reveal strong trends in neural decoding (2022 cross-subject decoding study), optogenetic methodology (2022 multichannel interrogation), and AI-neuroscience integration (2024 internal world models). Her work bridges basic neuroscience with applications for prosthetic devices and understanding movement disorders. Prof. Diester leads multiple DFG-funded projects including CRC 1690 on Disease Mechanisms of Sensory and Motor Systems. Her lab actively recruits PhD students for projects on prefrontal flexibility, movement decoding, and virtual reality effects on neural activity. The Optophysiology Lab maintains strong institutional connections through BrainLinks-BrainTools and IMBIT, recently developing the open-source FreiLaser system for cost-effective optogenetic experiments. The lab participates in major neuroscience symposia including the German Neuroscience Society meetings.

Xiaohui LiuView profile

Professor

• Intelligent Data Analysis
• Deep Learning
• Artificial Intelligence
• +7 more

Professor Xiaohui Liu is a distinguished Professor of Computing at Brunel University London, serving within the Computer Science department of the College of Engineering, Design and Physical Sciences. He maintains his office in the Wilfred Brown Building (Room 218) and has established himself as a leading figure in intelligent data analysis and artificial intelligence research. With over 20 years of academic leadership, Professor Liu has held significant visiting appointments including Honorary Pascal Professor at Leiden University (2004), Visiting Scientist at Harvard Medical School (2005), and Visiting Professor at the Chinese Academy of Sciences (2010). Professor Liu's research spans intelligent data analysis, deep learning, dynamical systems, human factors, innovative AI applications, optimisation, statistical pattern recognition, and trustworthy decision making. His work bridges theoretical advances with practical implementations across various industries, demonstrating exceptional translational impact. He has pioneered approaches that integrate artificial intelligence with data science to enable effective data interpretation and trustworthy decision-making systems, with applications spanning healthcare, manufacturing, and business domains. Analysis of Professor Liu's recent publications reveals a strong focus on transformer architectures, transfer learning, and optimization techniques applied to real-world problems. His work demonstrates consistent innovation in neural network architectures, particularly for anomaly detection, fault diagnosis, and recommendation systems. The publications show interdisciplinary applications spanning manufacturing, healthcare, digital marketing, and network science, reflecting his commitment to solving practical challenges through advanced computational methods. Clarivate Highly Cited Researcher for 11 consecutive years (2014-2024) World's top 2% of scientists by Stanford University (2020-2024) ScholarGPS Highly Ranked Scholar – Lifetime: Neural Network (2022-2024) Daniel Berg Award (2023) Research.com United Kingdom Leader Award in Computer Science (2023-2025) IDA Founders Award (2025) Professor Liu has secured substantial research funding from diverse sources including the European Commission, Innovate UK, Royal Society, and EPSRC. His current projects include AI-assisted tax assessment, intelligent data-driven pipelines for manufacturing certified metal parts, and maintenance models for zero-unexpected-breakdowns. He leads collaborative efforts through knowledge transfer partnerships with industry partners like Veritas Advisory Limited and has directed multiple European Commission-funded initiatives focused on IoT platforms, water resource management, and predictive maintenance systems. His research group actively mentors PhD students and collaborates with international partners across multiple continents. Professor Liu leads research activities within the IEHS and CSSB research groups at Brunel University, fostering interdisciplinary collaboration between computer scientists, engineers, and domain experts. His teams integrate expertise in neural networks, optimization algorithms, and statistical pattern recognition to develop innovative solutions for complex real-world problems. The research environment emphasizes both theoretical rigor and practical application, with strong industry partnerships ensuring that research outputs deliver tangible societal and economic impact.

• Topological Data Analysis
• Computational Neuroscience
• Neuroimaging
• +3 more

Manish Saggar is a Research Fellow at Stanford University specializing in the application of topological data analysis to human brain dynamics. His interdisciplinary work bridges neuroscience, mathematics, and computer science to develop novel computational frameworks for understanding complex neural systems. His core research interests include: Topological Data Analysis for neural networks Computational modeling of brain dynamics Advanced neuroimaging spatiotemporal representation Network neuroscience methodologies Mathematical frameworks for cognitive processes Dr. Saggar's publication record demonstrates consistent innovation in topological approaches to neuroscience, particularly through the development of the DyNeuSR framework and simplicial modeling techniques. His work reveals emerging trends in using algebraic topology to decode dynamic brain network properties, with significant implications for understanding neurological disorders and cognitive function. Collaborations with leading researchers like Olaf Sporns and Giovanni Petri highlight his integration within cutting-edge neuroscience consortia.

Hiromichi SuetaniView profile

Professor

• Neuroinformatics
• Computational Neuroscience
• Nonlinear Dynamics
• +8 more

Hiromichi Suetani is a Professor at the Department of Co-creative Science and Engineering , Faculty of Science and Engineering , Oita University . He holds concurrent positions as an Affiliated Researcher at the International Research Center for Neurointelligence , University of Tokyo , and has previously worked at institutions including RIKEN Center for Brain Science , ATR , and Kagoshima University . His research bridges nonlinear dynamics , machine learning , and neuroscience . Education : PhD in Informatics from Kyoto University, with research at the Institute of Statistical Mathematics and graduate training in Mathematical Engineering at the University of Tokyo. Research Society Affiliations : Society for Neuroscience Japan Neuroscience Society Physical Society of Japan His research focuses on decoding brain information and controlling complex systems using techniques like reservoir computing , topological data analysis , and nonlinear modeling . He investigates human EEG individuality , collective patterns in active matter , and chaotic synchronization in dynamical systems. Recent work with collaborators explores hybrid prediction models combining anticipating synchronization and echo state networks to improve time series forecasting in chaotic systems. Earlier studies analyzed EEG consistency under noisy stimuli and applied manifold learning to map brain oscillations. His teaching includes mechanics , computational physics , and nonlinear science at Oita University. He has secured competitive funding from the Japan Society for the Promotion of Science for projects on critical computation systems , stable chaos in neural networks , and topological analysis of biological data .

Paul De KoninckView profile

Professor

• Molecular Neurosciences
• Synaptic Plasticity
• Hippocampus
• +7 more

Paul De Koninck is a Professor in the Department of Biochemistry, Microbiology and Bioinformatics at Université Laval and Deputy Scientific Director of Fundamental Research at the CERVO Brain Research Center. His work bridges molecular neuroscience with advanced neurophotonics technologies to unravel mechanisms of synaptic plasticity and neuronal signaling. Academic Affiliation: Université Laval (Faculty of Science and Engineering) Leadership Role: Deputy Scientific Director, CERVO Brain Research Center Research focuses on synaptic plasticity , neurophotonics , and optical nanoscopy to study how neurons decode signals and remodel circuits for learning and memory . His lab develops cutting-edge tools like STED microscopy and single molecule imaging to track synaptic molecules in real time. Recent publications highlight his dual focus on neurodegenerative disease mechanisms (e.g., Huntington's disease) and microscopy innovation (machine learning-enhanced STED, fluorescence lifetime imaging). Articles span zebrafish neurobiology , mitochondrial transport regulation , and neurovascular framework development . Scientific Awards: 2018 Université Laval "Professeur étoile" Prize for teaching excellence His lab contributes to major collaborative initiatives, including the Canadian Optogenetics and Vectorology Foundry grant (2021: $4.3M from Brain Canada) and the Greenspine Lab for neurophotonics research. The team combines patch clamp electrophysiology , biochemistry , and quantum dot imaging to study synaptic signaling.

Teresa WilcoxView profile

Professor

• Infant Cognition
• Developmental Psychology
• Neuroimaging
• +4 more

Dr. Teresa Wilcox is a Professor in the Department of Psychology at Florida Atlantic University's Charles E. Schmidt College of Science. Her research focuses on infant cognition, perception, and the functional organization of the infant brain, particularly in understanding social and mechanical entities. She employs advanced methodologies such as violation-of-expectation, eye tracking, action tasks, and functional near-infrared spectroscopy (fNIRS). Ph.D., Psychology, University of Arizona (1993) M.S., Child Development, University of California, Davis (1988) B.A., Psychology, Bethel University (1985) Her work explores how infants acquire knowledge about physical, mechanical, and social entities, with a strong emphasis on neural activation patterns and behavioral responses. Recent publications highlight her expertise in fNIRS signal analysis, infant-mother synchrony, and object individuation. Dr. Wilcox leads the Infant Cognition and Language Lab (ICLAB), where her team investigates brain dynamics in early development. Dr. Wilcox's research trends include tensor decomposition for fNIRS data, social perception in neuromarketing contexts, and the role of visual cues in infant cognition. Her work bridges cognitive science, neuroscience, and developmental psychology, contributing foundational insights into early knowledge acquisition.

Sheila FlanaganView profile

Lecturer

• Auditory Neuroscience
• Developmental Dyslexia
• Speech Processing
• +3 more

Dr. Sheila Flanagan is an academic researcher affiliated with the Department of Psychology at the University of Cambridge and a Research Associate at the Centre for Neuroscience in Education since 2012. She serves as Director of Studies in Psychological and Behavioural Sciences and Bye-Fellow of Selwyn College. Ph.D. in Experimental Psychology (University of Cambridge) MSc in Music Technology (University of York) Background in psychoacoustics from engineering experience Her research focuses on auditory neuroscience, developmental dyslexia, and speech processing through neural entrainment. Key projects include the Botnar project (assisted listening tech for dyslexia), BabyRhythm project (auditory rhythm processing in infants), and studies of temporal sampling theory in speech encoding. Her work combines EEG analysis, motion capture, and computational modeling across neurotypical and atypical populations. Recent publications highlight trends in decoding speech from neural data, binaural temporal fine structure sensitivity, amplitude rise time processing, and cross-sectional studies of language development in Spanish-speaking contexts. She examines how cortical oscillations track speech rhythms across different modalities (acoustic/visual) and developmental stages. She collaborates with researchers such as Usha Goswami (PI of lab group), Kanad Mandke , and Áine Ní Choisdealbha . Her methodological expertise includes auditory perception studies, speech enhancement algorithms, and longitudinal neuroimaging.

Wei WangView profile

Professor

• Big Data Analytics
• Machine Learning
• Data Mining
• +5 more

Wei Wang is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles (UCLA) , and the founding Director of the Scalable Analytics Institute (ScAI) . She is also a core faculty member of the UCLA Jonsson Comprehensive Cancer Center, Institute for Quantitative and Computational Biology, and Bioinformatics Interdepartmental Graduate Program. Education: Ph.D. in Computer Science, UCLA (1999) M.S. in Systems Science and Industrial Engineering, SUNY at Binghamton (1995) Research Interests: Dr. Wang’s research spans big data analytics , machine learning , data mining , natural language processing , bioinformatics , computational biology , and AI for science . Her work focuses on designing scalable algorithms for complex pattern discovery in large-scale, heterogeneous data, with applications in medicine, genomics, and scientific discovery. Scientific Awards & Honors: ACM Fellow (2020) IEEE Fellow (2022) NSF CAREER Award (2005) Microsoft Research New Faculty Fellow (2005) Phillip and Ruth Hettleman Prize (2007) Okawa Foundation Research Award (2013) ACM SIGKDD Service Award (2016) IEEE ICDM Outstanding Service Award (2012) IBM Invention Achievement Awards (2000, 2001) Multiple Best Paper Awards at SIGKDD, ICDE, and ACM BCB Advising & Research Team: Dr. Wang currently advises 15 PhD students and 1 postdoctoral fellow . Her lab focuses on AI-driven scientific discovery, scalable analytics, and interdisciplinary applications in biomedicine and materials science. Labs & Institutes: Director, Scalable Analytics Institute (ScAI) , UCLA Member, UCLA Jonsson Comprehensive Cancer Center Member, Institute for Quantitative and Computational Biology Member, Bioinformatics Interdepartmental Graduate Program

Valentina GrecoView profile

Professor

• Cell Biology
• Dermatology
• Genetics
• +7 more

Valentina Greco is a Professor at Yale University, with appointments in the Departments of Genetics, Cell Biology, and Dermatology. She is a member of the Yale Stem Cell Center and Yale Cancer Center, and her research focuses on tissue regeneration, stem cell dynamics, and oncogenic mutations using the skin as a model system. Her lab develops novel in vivo imaging techniques to study how stem cells maintain tissue homeostasis amidst cellular turnover, injury, and mutations. Education: BS in Molecular Biology (University of Palermo, 1996), PhD in Cell & Molecular Biology (Heidelberg University, 2003), Post-doctoral Associate (Rockefeller University, 2009) Dr. Greco’s research explores the interplay between stem cell niches, tissue repair, and cancer initiation. Her work has uncovered mechanisms like positional fate determination in hair follicles, phagocytic clearance of stem cells, and metabolic adaptations to oncogenic mutations. Recent studies highlight her innovative use of live mouse imaging and machine learning to decode complex cellular behaviors. Her publications reveal trends in stem cell niche regulation , ERK signaling in oncogenesis , calcium dynamics during repair , and interdisciplinary science-art frameworks . She integrates molecular biology, computational modeling, and physiological imaging to address questions across developmental biology, cancer, and regenerative medicine. Scientific Awards: 2025 American Academy of Arts & Sciences 2021 ISSCR Momentum Award 2019 NIH Director’s Pioneer Award 2018 Carolyn Slayman Endowed Professorship 2022 FASEB Mid-Career Investigator 2021 Connecticut Academy of Science and Engineering (CASE) Member Dr. Greco prioritizes mentoring and inclusivity, fostering a collaborative lab environment that emphasizes trainee development and accessibility in science. She is also a dedicated public servant, serving as Vice President of the ISSCR, and her work has been recognized for its impact on understanding tissue homeostasis and tumor suppression mechanisms.

• Quantum Computing
• Machine Learning
• Error Correction
• +2 more

Arshpreet Singh Maan is a Doctoral Researcher at the Department of Computer Science , Aalto University. He is affiliated with the research group led by Professor Alexandru Paler, focusing on quantum computing and machine learning applications in error correction. Quantum Computing Machine Learning Error Correction Research Interests : Arshpreet’s work centers on quantum error correction algorithms, graph neural networks for quantum systems, and resource estimation for superconducting qubit architectures. His publications highlight contributions to scalable decoding methods for quantum codes and performance optimization in fault-tolerant quantum computing. Recent Article Trends : His research spans quantum machine learning, surface code decoding, and modular quantum system design. Key topics include graph neural networks for QLDPC codes, parametric-resonance gate error analysis, and utility-scale superconducting qubit requirements. Contact : Email: arshpreet.maan@aalto.fi

Alexandro Diaz RamirezView profile

Assistant Professor

• Decision-Making Mechanisms
• Neural Circuits
• Oculomotor Circuits
• +3 more

Alexandro Diaz Ramirez is an Assistant Professor at the Department of Physiology and Pharmacology within SUNY Downstate Health Sciences University's College of Medicine. His research focuses on decoding cellular-resolution neural circuits involved in decision-making, using larval zebrafish oculomotor circuits as a model system. Academic Rank: Assistant Professor Key Research Area: Integration of sensory signals and internal neural activity in decision-making Model Organism: Larval zebrafish Ramirez's research combines cellular calcium imaging and electron microscopic reconstruction to analyze neural ramping patterns preceding saccades. His lab investigates how oculomotor circuits sustain decision-related activity and how this mechanism is conserved across species. Key article themes include visuomotor behaviors , neural integrators , retinal development , and computational models of decision thresholds . His publications demonstrate interdisciplinary approaches spanning molecular genetics to systems neuroscience. Current research directions involve mapping hindbrain circuit dynamics and studying how self-initiated decisions emerge from neural activity patterns.

David BortonView profile

Associate Professor

• Neuroengineering
• Brain-machine interface
• Spinal cord injury
• +3 more

David Borton is an Associate Professor at Brown University in the School of Engineering , with additional appointments in Neurosurgery and Brain Science . He leads the Institute for Biology, Engineering and Medicine (I-BEAM) as Interim Director, focusing on neuroengineering and brain-machine interfaces for treating spinal cord injury and neuropsychiatric disorders . His research laboratory develops implantable neural interfaces , kinematic sensors , and biochemical sensors to study neuromotor diseases and create high-resolution motion capture systems. He has received the Marie Curie International Fellow award and is supported by DARPA , NINDS , NIMH , and Brown University Seed funds . Key research themes include contextual dependence of cortical circuits , wireless neural telemetry , and adaptive neuromodulation . His work bridges engineering , neuroscience , and clinical applications , particularly for spinal cord injury and Parkinson's disease .

Julia BerezutskayaView profile

Assistant Professor

• Brain-Computer Interfaces
• Neurotechnology
• Decoding Brain Signals
• +4 more

Julia Berezutskaya serves as an Assistant Professor at Utrecht University within the Brain strategic program, leading the Utrecht-BCI lab dedicated to developing neurotechnology for individuals with motor impairments. Her research focuses on creating brain-computer interfaces (BCIs) to enable communication and computer control for people with severe movement disabilities, emphasizing patient-centered artificial intelligence applications in medical care. Her primary research areas include decoding neural signals for BCI applications, developing specialized BCI software and interactive games, and integrating functional electrical stimulation to reanimate paralyzed muscles. This work bridges fundamental neuroscience with clinical neurotechnology, targeting neurological and psychiatric disorders through innovative neuroengineering solutions that translate brain activity into actionable outputs for rehabilitation. Recent publication trends reveal a strong emphasis on electrode optimization for sensorimotor cortex recordings (2024), hybrid BCI-FES systems for motor restoration (2023), and advanced deep learning models for speech reconstruction from neural data (2023). These contributions demonstrate consistent progress in making BCIs more reliable, clinically applicable, and responsive to user needs across neurorehabilitation contexts. The Utrecht-BCI lab operates at the intersection of neuroscience, engineering, and AI, with a mission to elucidate neuronal mechanisms underlying healthy brain function to develop transformative neurotechnologies. Current projects focus on enhancing BCI accessibility through user-centered design while addressing ethical considerations in patient-centered AI deployment for neurological care.

• Natural Language Processing
• Image Captioning
• Multimodal Dialogue
• +5 more

Dr. Ece Takmaz is a Researcher at the Department of Languages, Literature and Communication , Utrecht University, affiliated with the Humanities and Institute for Language Sciences . She works in the Language, Logic and Information division under Jakub Dotlacil and Albert Gatt, focusing on Natural Language Processing , Multimodal Dialogue , and Eye-Tracking research. PhD, Institute for Logic, Language and Computation (ILLC), University of Amsterdam (2019-2024) M.Sc., Artificial Intelligence, University of Amsterdam (2017-2019) M.Sc., Cognitive Science, Middle East Technical University B.Sc., Computer Engineering, Bilkent University Her research investigates the intersection of vision and language integration , with a focus on eye-tracking and perception-action dynamics . Her work includes developing multimodal contextual models , analyzing CLIP's cognitive plausibility , and studying human signal variation in visuo-linguistic tasks. She has contributed to frameworks for audience-aware dialogue adaptation and transformer-based reading behavior prediction . Recent publications cover topics like semantic violation analysis (COOCO dataset, 2025), emotion decoding in art (CogSci, 2024), and visuo-linguistic predictability (EACL, 2024). She has received recognition for best shared task paper (CMCL, 2022). Best Shared Task Paper Award, CMCL 2022 Dr. Takmaz actively contributes to academic service as an Area Chair for CoNLL 2025 and reviewer for top NLP/ML conferences. She serves on the organization committee for CMCL 2024-2025 and the EACL Student Board (2020-2022).

Michel BesserveView profile

Professor

• Causal Machine Learning
• Artificial Intelligence
• Complex Systems
• +3 more

Michel Besserve is a Full Professor in the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research bridges artificial intelligence, causal inference, and neuroscience to develop trustworthy and interpretable AI systems for understanding complex phenomena in artificial, physical, and socioeconomic systems. Dr. Besserve completed his PhD dissertation titled Analyse de la dynamique neuronale pour les Interfaces Cerveau-Machine : un retour aux sources at Université Paris-Sud 11 in November 2007. His academic journey has led him to become a leading researcher in causal machine learning, collaborating extensively with Bernhard Schölkopf and other prominent scientists in neuroscience and AI. Professor Besserve's research centers on causal machine learning, with a focus on understanding and anticipating changes in complex systems. He investigates principles like the Independence of Causal Mechanisms (ICM) to improve causal model identifiability and develop more robust AI. His work spans theoretical foundations of causal inference to practical applications in neuroscience, brain function analysis, and socioeconomic systems. He has made significant contributions to understanding brain networks through causal inference and machine learning, with publications in major journals including Nature, PLOS Biology, and Neuron. His publication record reveals a clear trajectory from theoretical causal inference toward developing frameworks for real-world applications. Recent work focuses on building Causal Computational Models (CCMs) that integrate data, domain knowledge, and causal structure to improve robustness and interpretability of complex system models. His research shows increasing integration of causal machine learning with applications to neuroscience and socioeconomic systems, particularly in developing causal AI that can address real-world complexity while producing interpretable outcomes for decision makers. Through his leadership in the Department of Empirical Inference, Professor Besserve has established a research program that bridges theoretical machine learning with practical applications in neuroscience and complex systems. His team develops novel causal machine learning tools that uncover internal structure and transformations of complex systems, with potential applications ranging from brain function analysis to sustainable economic modeling.